Canopus leverages vessel data and measurements from custom sensors to monitor system health and performance. While our current focus is on electrical engines, the platform’s architecture supports broader applications across various systems.
Canopus leverages vessel data and measurements from custom sensors to monitor system health and performance. While our current focus is on electrical engines, the platform’s architecture supports broader applications across various systems.
For electrical engines, fault patterns indicate that 88%-92% of issues occur in primary components—such as the stator, rotor, and bearings—while the remaining 8%-12% stem from other causes. Identifying and characterizing these faults allows for early detection and targeted maintenance, reducing downtime and preventing costly failures. This structured approach not only enhances reliability in electrical engines but also provides a foundation for applying predictive maintenance to other vessel systems
Custom sensor setups—incorporating up to three current transformers per device—are installed on an IoT board. These sensors collect data at a 4 kHz sampling rate and transmit measurements via the MQTT protocol to an onboard server.
Canopus is optimized for electrical engine diagnostics, covering motors, generators, pumps, and similar equipment. However, its flexible design allows for monitoring a wide range of systems throughout the vessel.
Data is compressed onboard for efficient cloud transfer. Once in the cloud, it is decompressed, stored, and processed through a dedicated data pipeline. This pipeline generates frequency spectra and other diagnostic outputs that enable actionable insights for maintenance planning.